{
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  },
  "name": "",
  "signature": "sha256:e036f01811b330cba8fd80eca762ea55a5f84c232a4e3c7ccb084d004624758a"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib.pylab as plt\n",
      "import os, sys\n",
      "import numpy as np\n",
      "import pickle as pkl\n",
      "from docopt import docopt\n",
      "from itertools import product"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "def _best(m):\n",
      "    r = np.zeros(len(m))\n",
      "    for i in range(len(m)):\n",
      "        r[i] = np.max(m[:i+1])\n",
      "    return r"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(figsize=[5.2, 3.4], dpi=130)\n",
      "plt.style.use('ggplot')\n",
      "t = np.load(os.path.join('../logs', 'BCentropy-apd:cnn-globe-bn-X-sgld-mnist-2-X-mnist@2018-05-28', 'acq_acc.npy'))\n",
      "acq_arrs = np.array(t)\n",
      "m = acq_arrs\n",
      "plt.semilogx(np.arange(len(m))+1, _best(m),'.-', c='C3', label='Ours,Active')\n",
      "t = np.load(os.path.join('../logs', 'BCentropy-cnn-globe-bn-X-sgld-mnist-2-X-mnist@2018-05-27', 'acq_acc.npy'))\n",
      "acq_arrs = np.array(t)\n",
      "m = acq_arrs\n",
      "plt.semilogx(np.arange(len(m))+1, _best(m),'.-', c='C4', label='SGLD,Active')\n",
      "t = np.load(os.path.join('../logs', 'DCentropy-mcd:cnn-globe-bn-X-sgld-mnist-2-X-mnist@2018-05-28', 'acq_acc.npy'))\n",
      "acq_arrs = np.array(t)\n",
      "m = acq_arrs\n",
      "plt.semilogx(np.arange(len(m))+1, _best(m),'.-', c='C5', label='MC-Drop,Active')\n",
      "plt.xlabel('num acquisition (T)')\n",
      "plt.ylabel('best test acc up to T (/1.)')\n",
      "plt.legend(loc='lower right')\n",
      "plt.ylim([.3,1])\n",
      "\n",
      "# plt.savefig('AL-cs.png', bbox_inches='tight')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "(0.3, 1)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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jF4TeQSTbHsBkMrF79278/PwYNWpUm6+jkGrwyfsAVW0xZX3uRGs28F32Wkuh\n7MiA+SLRCkIbiTXIeoBDhw5RUVHB1KlTUanauNaVbMJbuxl1TQ7lIb+hzi2CzOLPriqEDUX6U3aK\nWBB6H5uS7YsvvkhmZqbVtszMTNavX98hQQm2q6io4ODBgwwePJjw8PC2XUSW8MpPwdmQRWXQzdR6\njKCwOhNt1VFAgQKlKIQtCO1kU7I9efIkw4YNs9o2dOhQyzI2guPs3r0bhULR9jHPsoxn0VZcq09Q\nGTAHo9c49HU60i+/hqdzCFP7P8HIoJttWhVBEISm2dRm6+TkhNFotOp4MRqNbf+TVbCLS5cuce7c\nOWJiYvD09GzTNdx1X6GpOEi1bzwGn8mYpFr2XHoFs1zH5H6P4OXShxAPUchdENrLpifb0aNH8847\n76DX64H6qbrJyckOWaFVqGc2m/n+++/x8vJi7Ni2jXd1K/0e97Jd6L0mUO03o34KrnYDpcYLxPRd\nhpeLbQU2BEFomU1PtrfffjuvvfYaS5cuxcPDg6qqKsaMGcNDDz3U0fEJTTh69CilpaXMnTu3TSvl\nupbvx0P3FUaP66kKnAcKBWd1X3OxbA8jAhfS10tMWBAEe7Lpt9TDw4OVK1dSVlZGcXExAQEBnbqW\nlWCturqaffv20b9/fwYObH0Bbpeq43gWbaPGbSgVwYtAoaSwOpOM/H/Qx3MsIwIXtHwRQRBaxaZk\ne/ToUQIDA+nTp48lyebl5VFcXCzqIzhAeno6ZrOZuLi4VtclcNafwSs/hTrXfpSH/BYUakuHmIdz\nMBP63odCIUYECoK92fRblZycjEajsdrm6upKcnJyhwQlNO3EiRNkZmYyZMgQfH19W3Wu2pCNt3Yz\nJucgykOXgNK5UYeYs6rts88EQWiaTcm2vLy80S+2r68vZWVlHRKUcG0ZGRmWpeOzsrLQarU2n6uq\nycdHuxGz2pPyPnciqzSiQ0wQOpFNzQjBwcGcOHGCkSN/GgL0448/EhQUZPONMjIy2LBhA5IkkZCQ\nwIIFjdsF09PT+eSTT1AoFPTv358//OEPNl+/J9PpdKSlpXHx4kXLNkmSyMnJITQ0tMXzlXUl+OS9\nj6xwoqzPXUjq+mFiWSWpokNMEDqJzSUWX3zxRaZPn05wcDAFBQV8++23PPDAAzbdRJIkkpOTWb16\nNf7+/qxcuZKoqCjCwsIsx2i1WrZt28Zf/vIXPDw8KC8vb9s76kH0ej379u3jxIkTODk5MWrUKDIz\nMzGbzahUKqvvX1OUpgp8c5NRyCZK+96H5FS/hHxh9SmO5P+DPp43iA4xQegENiXb6OhoVq9ezc6d\nOzl8+DD+/v48+eSTDB482KYmOcqRAAAgAElEQVSbZGVlERISQnBwMACxsbEcOHDAKll88803zJo1\nCw8PDwC8vb1b+156DJPJREZGBgcOHMBkMjFq1CjGjx+Pm5sb1113HTk5OYSFhbX4VKswG65U8Kqi\nrO9dmF3qv//1HWKv4uEcxIS+94sOMUHoBDYP0Bw8eLDNyfXnSkpK8Pf3t7z29/fn7NmzVsc0rJL7\n1FNPIUkSixYt6nWTJmRZ5syZM6Snp1NZWcnAgQOZNGkSfn5+lmNCQ0NtajpAqsVH+wGq2iLK+tyB\nybUfAGaplrTLr4oOMUHoZDYn24sXL5KZmUllZSVXr36+ePFiuwQiSRJarZZnnnmGkpISnnnmGV58\n8UXc3a1L+qWmppKamgpAUlISAQEBrbqPWq1u9TmdITs7m6+++orc3FxCQkK4+eabGTRoUNsuJplQ\nnH4djJeRh96Pt199e6wsy+w8/TIlhvP8YsTTDAoQw/Ycpat+DoWOY1OyTU1NZePGjVx//fVkZGQw\nZswYjh07RlRUlE038fPzQ6fTWV7rdDqrp7WGY4YMGYJarSYoKIjQ0FC0Wm2jp+nExEQSExMtr4uL\ni22KoUFAQECrz+lIZWVlpKWlce7cOdzd3UlMTOS6665DqVS2LU5ZwqsgBdeqH6kIugmj1A+uXOes\nbgenCnYwInABXgzpUt+H3qarfQ6FtuvTx7ZRPDYl2+3bt7Nq1SqGDx/OnXfeyeOPP86RI0dIS0uz\n6SYRERFotVoKCwvx8/MjPT2dhx9+2OqY8ePHs2fPHuLj46moqECr1VraeHsio9HIgQMHOHr0KEql\nkgkTJjB27FicnJzaflFZxrNoO65Vx6jy/wVGr2jLLusOsYV2eAeCILSGTcm2oqKC4cPra5kqFAok\nSeKGG27g1VdftekmKpWKpUuXsmbNGiRJIj4+nvDwcFJSUoiIiCAqKorRo0dz9OhRHn30UZRKJbfd\ndlubK1l1ZWazmePHj7N//36MRiORkZHExMRYOgbbw73kazQV+6n2mYreN86y/acZYoGiQ0wQHMSm\nZOvn50dhYaHlz/uDBw/i6enZqgIoY8eObVSd6ur2XoVCwZIlS1iyZInN1+xOZFnm/PnzpKWlUVZW\nRnh4OJMnTyYwMNAu19eU7sa99DsMXuOp9p9l2f5Th1gtk/utEh1iguAgNmXL+fPnk5ubS1BQELfc\ncgsvvfQSJpOJO++8s6Pj6xEKCgrYvXs3eXl5+Pr6Mm/ePPr379/qugZNca04iKfuvxg9RlEZOB+u\nXFeWZQ5qP6DEcJ7J4Y+IFXEFwYEU8tVDC2xkMpkwmUy4urp2REyt0jBkzFad2TFRWVlJeno6p0+f\nRqPRMGHCBEaOHIlSab8/412qTuCV/xG1msGU97kdFD/9+3lWt4PD+ZsYEbiAkUE32+2eQvuJDrKe\nw64dZI1OUqvbVEO1t6itreXQoUMcPnwYgHHjxhEVFYWLi4td7+Okz8Ir/1+YXMMpD73NKtFaOsQ8\nxogOMUHoAkTGtCNJkjh58iR79+5Fr9czdOhQYmNj8fLysvu91MbLeGs/xOwcQNmVCl4N9HUlP3WI\nhS0THWKC0AWIZGsn2dnZ7NmzB51OR2hoKHPnziUkJKRD7qWqLcAnbwOyyoOyPkuRr+r0qu8Qe0V0\niAlCF9Orkq3acAFyfkAtB1umr7ZXiU7HnrQ0Ll++jLeXF3N+OYtBgwbVd37JJrvc42ouVSfxLPwP\nskJFad+7kNQ/PTXXl0zcSInhPJPC/yA6xAShC7Ep2d55551s2LCh0fa7776b9957z+5BdQS1IRvf\n3PdQIOHX8uE2CwKuGwGMAMgHzsB5O97gGmRAgRqlqdJSxQsgq/QbLpTtIjJwAWFets3uEwShc9iU\nbM1mc6NtJpMJSZLsHlBHcTZcoD5N1f9/rWYIdZrW1x4wS2a0Wi25OTlIskxISAhhYWE4qdsx88tG\nTobzOBvOogBk2Yyz4QImTX8AiqpPc0S7mT4eYxgpOsQEoctpNtk+/fTTKBQK6urqeOaZZ6z26XQ6\nhg4d2qHB2VOtZiDuCjWybAaFimq/BEuisoUsy5w6dYoffviBqqoqBg0ayaRJk/D19aUOqOu40C3U\nhoE4511Elk2gUFOrqV/sUV9XQtrlV0WHmCB0Yc0m2+nTpwP19Wjj4+Mt2xUKBd7e3lYrN3R1Jk1/\nSvvchY+ygDIpuFWJNicnhz179lhm0c2cOdOmwt321vAenA0XqNUMxKTpb9UhNilcdIgJQldl06SG\n3Nxc+vbtmp0tHTmpoaysjD179nD+/Hk8PDyIjY1l2LBhdpv51V6yLHMg7z0ulO1iUvgfRDttNyIm\nNfQcdp3UcOHCBWRZJiwsjLy8PN5++22USiV33313l03C7WEwGNi/fz/Hjx9HpVIxceJExowZ076K\nXB1AdIgJQvdhU+NeSkqKpSrVpk2biIiIYPjw4d1mJIKtTCYThw8fZtOmTRw7dozIyEhuv/12oqOj\nu1yibegQCxUdYoLQLdhcYtHHx4fa2lpOnz7N8uXLUalU3HXXXR0dX6eQZZmsrCzS0tKoqKigX79+\nTJkyxWopn66koUPM3TmQmDBRMlEQugObkq2Xlxf5+flcunSJiIgInJycqKmp6ejYOkV+fj67d+9G\nq9Xi7+/P/Pnz6d/f9s6zzlZYdZK9uX/HJBmJH7AKZ5V7yycJguBwNiXbm2++mT/+8Y8olUoeffRR\nAI4fP96lk1JLKioqSE9P58yZM2g0GqZPn05kZKRdK3LZW1H1Gb7LXoeMhFKhpk7SOzokQRBsZHOJ\nxYYn2YbKVeXl5ciyjI+PT8dFZ4PWjEbQarUUFRWh1WrJysoC6ouajxs3Dmdn5xbOdrzvLiZRUP0j\nAAqUjAy6mcjAeQ6OSmgLMRqh57B7icXa2lqOHDlCaWkp8+fPx2w205pSuBkZGWzYsAFJkkhISGDB\nggVW+7/77js+/PBDy0KQs2fPJiEhwebrt0Sr1bJlyxbLrLf+/fszffr0brP0zoWyPRRU/4jiSp+m\nUqEmyH24g6MSBMFWNiXbkydPsn79egYNGsTp06eZP38++fn5fPrpp6xYsaLF8yVJIjk5mdWrV+Pv\n78/KlSuJiopqNDEgNja2wzrdcnJyLIlWoVDQp0+fbpNo86tOcCD3PYLdRxAZsIBiwxmC3IcT4DbE\n0aEJgmAjm5LtBx98wCOPPMKoUaMsS+EMHjyYc+fO2XSTrKwsQkJCLKvlxsbGcuDAgU6dhRUWFoZa\nrcZsNqNSqRwyA6wtyoyXSLv8Cl4ufYgNfxhnlRtBHtc5OixBEFrJpmRbVFTEqFGjrE+8krhsUVJS\nYjWMyt/fn7NnzzY6bt++fWRmZhIaGsqSJUsICAiw6fq2CA0NZeHChZSWluLr60toaKjdrt1R9HUl\n7Mp+ESelhrj+j4mpuILQjdmUbMPCwsjIyGDMmDGWbcePH6dfP/vUhIX6pWMmTZqEk5MTO3bs4I03\n3mhU/AYgNTWV1NRUAJKSklqVkAMCAlCr1ZhM9q8za281pmp2ZLyMWa5h4ZgXCfAY6OiQhHaSZZmS\nkhJMJhOFhYWt6vMQHE+tVuPn59fm6fo2Jdvf/e53rFu3jhtuuIHa2lreeecdDh06xOOPP27TTfz8\n/NDpdJbXOp3O0hHW4Or204SEBDZv3nzNayUmJpKYmGh53doe3e7QC2yWTOy69Dyl1ZeZ2v9xMHpS\nbOzaMQstMxgMODk5Wdbw6w7/6As/qaurIycnB41GY7Xd1tEINg0qHTp0KC+88ALh4eHEx8cTFBTE\nc889x+DBg226SUREBFqtlsLCQkwmE+np6URFWc/lLy0ttXx98ODBbtOmam8NxWUKqzMZ3/dugj1G\nODokwU4kSRILpXZjarW6XTW8bfrJf/rpp8ybN4/58+dbbf/888+ZO3dui+erVCqWLl3KmjVrkCSJ\n+Ph4wsPDSUlJISIigqioKL788ksOHjyISqXCw8ODBx54oG3vqJs7XvhvssvTGBV0CwN8Jjs6HMGO\nukq1OKHt2vMztGlSw5IlS9i4cWOj7U0tl9OZOrLEYmc7V7KTg9oNDPKdRlToUvHL2cPo9Xrc3Oo7\nOR3VjJCXl8eTTz7JmTNnkGWZxMREVq9e3SGTepYuXUphYSGff/55s8eVl5ezdetW7rjjDqB+Cv1T\nTz3Fu+++a/eY2uvqn2EDuzQjnDhxghMnTiBJkuXrhv998803jdouhLbLqzzCIe0HhHqMZlzoHSLR\nCnYnyzL33HMPs2fPJi0tjd27d1NdXc26detsvoatI5DKy8s5duwYlZWVZGdnN3tsRUUFmzZtsrwO\nCQnpkom2vZptRnjrrbeA+tljDV9D/aO0j48PS5cu7djoegmd4Tzpl1/Hx3UAE8MeRKlQOTokoQfa\ns2cPLi4uLF68GKhv3nv22WeJiYkhPDycs2fPsmbNGgBuv/127r//fmJjYxkyZAi33XYbu3fv5rnn\nniM1NZWvv/4atVpNXFwcTz/9dKN7ffnll8yYMYPAwEC2b9/Oww8/DNQPI12xYoUlAa9du5b333+f\n7OxsZsyYQVxcHHfccQdLlixh586dzJ07l/Xr1zNs2DAAbrnlFp566imGDBnC6tWrOX36NHV1dSxf\nvpxZs2Z1xrexzZpNtm+88QYAr7/+Og8++GCnBNTbVNUWsjt7Pa5qL6b0+z+cVK6ODknoQrRaLTk5\nOYSFhbV7bPiZM2cajZf39PSkb9++zT6x6vV6brjhBp555hlKSkpYvnw5u3btQqFQUF5efs1ztm3b\nxqOPPkpAQAD33nuvJdk+9dRTxMTEkJycjNlsprq6mlWrVnH69Gl27NgBwOXLly3XmTdvHp999hnD\nhg2joKCAgoICRo8ezdq1a5k0aRIvvfQS5eXlzJkzhylTpjT6E78rsamDTCTajlFjqmRX9gvImInr\n/zgaJ8cW9RE6z7fffkthYWGzx9TU1KDT6ZBlGYVCgb+/v6UQ1LUEBgYSFxdn71BRqVTMmTMHqC+3\n6uLiwvLlyxsNw2xQVFTEhQsXGD9+PAqFArVazalTp7juuutIS0vjlVdesVzXy8uryYQNcOONN3Lr\nrbfy2GOP8dlnn1ni2LVrFzt27ODvf/87UP+9ys3NZciQrjuFvevWE+zhTFItey7/jeo6HZPDH8XL\nxbZGdqH3qK2ttUx8kGWZ2tradl1vyJAhHD9+3GpbZWUlubm5eHl5WQ1rurpetYuLCypVfdOWWq3m\niy++YM6cOaSmpvLb3/620X0+++wzysvLiYmJYcKECeTk5LBt27Y2xRwaGoqvry8nT560jIqC+u/H\nO++8w44dO9ixYwcHDhzo0okWWlH1S7AfWZbYl/t3ivVniQ37PYHuwxwdktDJ4uPjWxyNoNVq2bp1\nq6Wex6xZs9rVlDBlyhTWrl3LJ598wqJFizCbzfz5z3/mV7/6Ff379+fDDz9EkiS0Wi0ZGRnXvEZ1\ndTUGg4GEhASio6OZOHEiUN9Gm5GRwcqVK9m2bRubN2+2jKW/dOkSv/71r1mxYgWTJ09m06ZN3HPP\nPZZmBHd3d6qqqpqMe968ebz11ltUVlYSGRkJwNSpU9mwYQN//etfUSgUnDhxosuv9i2ebB0gI/8j\ncioOMCb4N4R7T3B0OEIX1VDPIyYmhoULF7a7zVahUPDee+/x+eefM2nSJKZMmYKLiwsrVqwgOjqa\nfv36MW3aNJ5++ulGbbsNqqqqWLJkCYmJiSxcuNAypT47OxsPDw8uX75Mbm4u48aNs5zTr18/PD09\nOXz4MH/+859JT08nISGB2bNnc+bMGfz8/IiOjmb69On85S9/aXTPOXPmsH37dm688UbLtkceeYS6\nujoSExOJj4/n+eefb9f3pjPYNM72iSeeuOabWbFiBUlJSR0SmK262zjb07qvyMj/B0P8ZnFDyG/F\nEK9epCuMs+0oDz30EM8++2yXXbfPXtozztamZoT8/PxG22RZpqCgwKabCPUuVxwgI/8jwjyjGBNy\nq0i0Qo/x2muvOTqELq/ZZPv6668D9Ut8N3zdoKioiPDw8I6LrIcp0p9hb85b+GsGMyFsGUqxIq4g\n9CrNJtuGYt8//1qhUDBs2DBL47jQvIoaLXsuvYybkz9T+j2KWtn11zsTBMG+mk22ixYtAuqHjFxd\ny1awndFUzq7sF1CgYGr/x3BRd4+leARBsC+b2mzVajWFhYUEBQVRVlbG5s2bUSqV3HrrrQ5fXbcr\nM0lGdmWvx2gqJ37AKjycg1s+SRCEHsmmhsPk5GSUyvpDN27ciNlsRqFQ8Pbbb3docN2ZJJtJv/wG\nZcaLTAz/Pf5uEY4OSRAEB7Ip2ZaUlBAQEIDZbObo0aPcd9993HPPPZw5c6aj4+uWZFnmsHYT2qoM\nxoYuoa/nWEeHJAgAvPLKK8THx5OYmMiMGTM4fPgwJpPJUmtgxowZzJgxwzKlFrjmzKz169czbtw4\nZsyYwaRJk7j77rubzQcmk4lRo0bx3HPPtRhjQ1XBBl9//XWjDvruyKZmBI1GQ1lZGZcvXyYsLAxX\nV1dMJlOPGidoT5nFn3GudCfDA+Yy2C/B0eEIAlC/AkpqaipfffUVLi4ulJSUUFtby/PPP09hYSHf\nfPMNrq6uVFVV2fRX6z333MP9998PwPbt2/nVr37FN998c82xtrt27WLQoEF8/vnnrFy5stlhjz/+\n+CPHjh0jIaH+d2fmzJnMnDmzje+667DpyXb27NmsXLmSV1991VLG7NSpU/Tt27dDg+uOLpalcbzw\nE/p5T2RU0CJHhyMIFoWFhfj5+VmK2fj5+eHt7c0//vEP/vrXv+LqWl9xzsPDg+XLl7fq2vPnzycu\nLo6tW7dec/+2bdu466676NOnDwcPHrRsz8jIYN68eSQmJjJnzhwqKip48cUX+fTTT5kxYwbbt28n\nJSWFJ598koqKCsaPH2+p4aDX64mKiqKuro6LFy/y29/+ltmzZ7Nw4UKysrLa8i3qUDY92S5YsIDx\n48ejVCoJCQkB6n9QDf+qCfUKqn7kQN67BLkNZ3yfe1CIsbRCO6kN2TgbLlCrGYhJ079d15o6dSov\nv/wykydPZsqUKcybNw9vb2/69u2Lh4dHu2MdNWrUNZOc0Whkz549rFu3joqKCrZv3050dDS1tbUs\nW7aMt956izFjxlBZWYlGo+Gxxx7j2LFjltq6KSkpQH3FsREjRvDDDz8wadIkduzYwbRp03BycuKJ\nJ54gKSmJQYMGcfjwYVauXMknn3zS7vdkTzYXogkKCuLs2bOcP3+e2NjYRqvjtiQjI4MNGzYgSRIJ\nCQksWLDgmsft3buXl156ibVr1xIR0X06lcqMl0m7/AoeziFM6vcHVEonR4ckdGFuBdtRGnKbPUYh\nGVHX5gMy7igwOYcgK5uud2xyCaUq8MYm97u7u/PVV1+xb98+0tPTWbZsGQ899JDVMSkpKbz33nuU\nlpayffv2Vv312tTM/9TUVGJjY9FoNPzyl7/kb3/7G3/60584d+4cQUFBlmGlV6+w3ZR58+bx6aef\nMmnSJD799FOWLFlCdXU1hw4d4r777rMc194KaR3BpmR76dIl1q1bh5OTEzqdjtjYWE6ePMn333/P\no48+2uL5kiSRnJzM6tWr8ff3Z+XKlURFRTVaQddgMPDll192+VJpP6evK2FX9ouola5M7f84zip3\nR4ck9AAKyQjIKAAZGYVkbDbZ2kKlUhEbG0tsbCzXXXcdmzdvJjc3l6qqKjw8PFi8eDGLFy9m+vTp\nrV5J9sSJE4wePbrR9u3bt7N//34mTKgvulRaWkpaWhqBgYGtjn/mzJkkJSVRWlrKsWPHmDRpEnq9\nHi8vL0vx8a7KpmT77rvvsnjxYuLi4rjzzjsBiIyMtHnoV1ZWFiEhIZZZaLGxsRw4cKBRsk1JSWH+\n/Pl8+umnrXkPDlVnNrAr+0XqJD3TB67GzalnF+IQ7EMfPL/FDma1IRvfvGRk2QQKNRXBi9vVlJCV\nlYVSqWTQoEFAfUdUREQEI0eO5Mknn2TdunW4urpiNptb/WT4xRdfsGvXLksVsLVr1zJmzBgmT57M\nvn37OHDggKWtOCUlhW3btpGUlERhYSEZGRmMGTOGqqoqXF1d8fDwaLLkoru7O6NHj+bpp58mMTER\nlUqFp6cn4eHhfPbZZ9x4443IsszJkycZMWJEm79XHcGmZJuTk8OUKVOstrm6utr8AykpKbHqofT3\n9+fs2bNWx5w/f57i4mLGjh3bbZKtWTKRdvkVKmryiOu/HF/X9rWpCcLVTJr+lPa5y25ttnq9ntWr\nV1NRUYFarWbAgAE8//zzeHp68sILL5CQkIC7uzuurq4sWrTI8nBkMBisSibee++9QP1D2JYtW9Dr\n9Vx33XV8/PHHlt/zzMxMZsyYwZdffsmkSZOsVpiYOXMmf/3rX1m7di1vvfUWq1evxmg04urqSkpK\nCrGxsbzxxhvMmDHjmqvEzJs3j/vuu49///vflm2vv/46K1eu5JVXXsFkMjF//vzumWwDAwM5f/68\nVRtqw9OqPUiSxKZNm3jggQdaPDY1NZXU1FQAkpKSCAgIaNW91Gp1q8+5FlmW+eb0egqqf2T6sP9j\neEh8u68p9GwFBQWo1T/9yl39dZM8I6j1rP+9a2+l/7Fjx/Lf//73mvuefvrpay7cCNeu+gfwxz/+\nscl7mc1mYmJiiImJ4dZbb7XaFxgYSGZmJgBRUVF89dVXjc7/+uuvrV5fvSLEggULGvX5DBo0yNKR\n1pFcXFzanD9s+vktXryYpKQkZsyYgclkYuvWrezYscOqQbo5fn5+6HQ6y2udTmfVwWY0Grl8+TJ/\n+tOfACgrK+P555/niSeeaNRJ9vN1j1pbm9Ze9WyPF/yb08XfMDLwJgLVNzi0Rq7QPdTU1FgtL9OT\nx6n/4x//6JHvr6amptHvul3r2Y4bN45Vq1bxzTffEBkZSVFREY899pil7aclERERaLVayzi/9PR0\ny2qbAG5ubiQnJ1teP/vss/zud7/rsqMRzpV8y8ni7Qz0mUpk4LVHVQiCIFzN5r9MBg4cyN13392m\nm6hUKpYuXcqaNWuQJIn4+HjCw8NJSUkhIiLCslZRd6CtPMoh7QeEeFxPVJ87RAFwQRBsYtOyOCaT\niS1btpCWlkZpaSm+vr7ExsZy00034ezs2NqsnbksTonhAt9eXIOHcwjTBzyJk0rTpusIvVNPXhan\nt+jwZXHeffdd8vLyuPPOOwkMDKSoqIitW7dSUlJiU6dWT1BdW8TuS+txVnkS1+8xkWgFQWgVm5Lt\ngQMHeO2113B3rx+sHxYWxpAhQxrNPumpakxVfJ/9AmapjvhBK9E4iRq+giC0jk2T9318fKipqbHa\nVltbi6+vb4cE1ZWYpVr2XH6Z6roiJvd7FC8XUXxH6L769u1r9ZDUUPrw9ttvt2zbuXMnv/jFL5g2\nbRozZ860jBL6uQkTJpCQkEBCQgLTpk1j3bp1GI3GDou9u5dpbPLJ9sSJE5av4+LieO6555g9ezb+\n/v7odDr+97//ERcX1ylBOoosS+zLfYdi/Rkmhv2eIPfrHB2SILSLm5sbp06dwmAwoNFo2LVrl9V4\n+VOnTrF69Wo2bdrE4MGDMZvNbN68ucnrffLJJ/j5+VFdXc0TTzzBH//4R6tauFCfJG0aU9yC7l6m\nscnvwFtvvdVo28/Lp6WmpjZZUKYnOFrwLy5X7GN08K/p5x3j6HAEwS6mT5/ON998w9y5c9m2bRsL\nFixg3759ALz55ps8/PDDDB48GKgfSbRkyZIWr+nu7k5SUhLR0dGUlpaSmZnJCy+8gLe3N1lZWezZ\ns4e3337bMvHgN7/5Dffccw+XL1/mt7/9Lddffz3Hjx9n6NChvPrqq2g0jftEGso0btq0iYMHDxId\nHQ3UF7l6+umn0ev1uLi48M9//pMXX3wRo9HI/v37efDBBzEajRw7dow//vGPJCYmsnfvXpRKJXq9\nnri4OH744Qdyc3N58skn0el0aDQaXnjhBcv3wR6aTLZvvPGG3W7SHZ3Rfc1p3ZcM9pvBMP9fOjoc\noZcq1p+lsDqTIPfhBLjZp0DT/Pnzefnll0lMTCQzM5Nf//rXlmR7+vRpmycr/VxDjYILFy4AcPz4\ncXbu3Em/fv04duwYH3/8MZ9//jmyLDN37lwmTpyIt7c3586dY/369URHR/N///d/bNy4sVH51p5Q\nprH9z/Y9UE7FQY7kb6av5zhuCLlNjKUV7O5g7kZK9BebPabObKCs5hIgAwp8XPo1OwrGx7U/Y0Nv\na/HekZGR5OTksH37dqZPn966wFtw9UjSMWPG0K9fPwD279/P7NmzLcOmfvGLX7Bv3z5mzpxJnz59\nLE+pN910E++//36jZNsTyjSKZPszxfoz7M15E39NBDFhy1CKAuCCg9RJeuoTLYBMnaS325DDmTNn\n8uc//5l///vflJaWWrYPHTqU48ePNyriYjabmT17tuXcxx9/vNE1q6qqyMnJYdCgQZw8ebLReNSm\n/Pxh5loPNz2hTKNItleprNGy+9LLaJz8mNzvUdRKl5ZPEoQ2iOq7pMVJDcX6s3x3MQlJNqFUqIkJ\nW2a3poTFixfj5eXF8OHDSU9Pt2xftmwZ99xzD9HR0URERCBJEps3b+b2229vNhFVV1ezcuVKZs2a\nhY9P46GREyZM4NFHH+XBBx9ElmW++uorXn31VQByc3M5ePAgUVFRbNu2zfKU29PKNIrHtiuMpnK+\nz34RBQri+j2Oq9rL0SEJvVyA2xCmDVjByKCbmTZghd0SLdTPerrrrrsabY+MjOTZZ5/l97//PVOn\nTmX69OlcunSpyessWrSI6dOnM2fOHPr27cu6deuuedyoUaNYtGgRc+bMYe7cufzmN79h5MiRQH3t\nlI0bNzJ16lTKy8stHXKZmZkEBgY2WaZxx44dyLJsKdOYmJjIr3/9a2pqaoiNjeXs2bOWdcx+bt68\nefznP/9h3rx5lm2vv73k7O8AAAuVSURBVP46//rXv0hMTCQ+Pr5R5bH2smm6bldmj+m6JsnItxfX\nUm7MIX7ASvzd7NcDKQgNxHTdxi5fvsySJUvYuXNno3233norH330kQOialp7puv2+idbSTbzQ86b\nlBouMDHsAZFoBaGL6GqJtr16dbKVZZnD2g/JqzzCDSG/o6/XuJZPEgTBbsLDw6/5VNsT9epke6r4\nC86VfsN1/r9kiP8MR4cjCEIP1muTbXZZOscKU+jnFcP1wYsdHY7QC3Tz7hGB9v0Me2WyLazOZH/e\nOwS6Xcf4vveiEGNphU6gVCpFp1g3ZjKZUCrbnit61TjbYv1ZMrO28GPel3g4hzC53yOolE6ODkvo\nJVxdXTEajdTU1ODq6tqokp7QdcmyjFKpxNXVtc3X6LRkm5GRwYYNG5AkiYSEhEYFbL7++mv+97//\nWd7QfffdR1hYmN3uX6w/y7cX1yLJdQCMCrwFZ5W73a4vCC1RKBSWAiv2WnhU6D46JdlKkkRycjKr\nV6/G39+flStXEhUVZZVMJ0+ebCmBdvDgQTZu3MiTTz5ptxgKqzMtiRYUVNS2bnyuIAhCe3RKY2VW\nVhYhISEEBwejVquJjY3lwIEDVsdcPVDYaDTavfhLkPtwVApnFChRKZwIch9u1+sLgiA0p1OebEtK\nSvD397e89vf35+zZs42O++qrr/jiiy8wmUw8/fTTdo2hYepjNZdwp59dpz4KgiC0pEt1kM2ePZvZ\ns2ezZ88etmzZwoMPPtjomNTUVFJTUwFISkqyeaocQB9sP1YQOlprPrtC99cpzQh+fn7odDrLa51O\nh5+fX5PHX6uZoUFiYiJJSUkkJSU1ef7bb7/d5L4VK1bYEHHX1tz76w73a+/12nJ+a86x5diWjmlp\nf3f/HHb2Z7Aj7tnZn8NOSbYRERFotVoKCwsxmUykp6cTFRVldYxWq7V8ffjwYUJDQ9t8v3Hjeva0\n285+f/a+X3uv15bzW3OOLce2dIz4DHb9e3b257DTqn4dPnyYjRs3IkkS8fHx3HTTTaSkpBAREUFU\nVBQbNmzg+PHjqFQqPDw8WLp0KeHh4XaPY8WKFc0+FQtCZxCfw96n25dYbK3U1FQSExMdHYbQy4nP\nYe/T65KtIAiCI4iiAIIgCJ1AJFtBEIROIJKtIAhCJ+hSkxocwWg08t5776FWqxkxYgRTpkxxdEhC\nL1NQUMB//vMf9Ho9y5cvd3Q4QgfpkU+2b775JnfffXejD25GRgZ/+MMfeOihh9i2bRsA+/fvJyYm\nhvvvv5+DBw86IlyhB2rNZzA4OJhly5Y5IkyhE/XIZDtt2jRWrVplta2h8tiqVat4+eWXSUtLIycn\nB51OR0BAAEC7CgMLwtVa8xkUeocemV0iIyPx8PCw2tZU5TF/f3/LVGIxCk6wl9Z8BoXeoUcm22u5\nVuWxkpISxo8fz759+3j33Xd7/BRLwbGa+gxWVlbyzjvvcPHiRbZu3fr/7d19SJPdG8Dx79zaFqaT\nWWSmqRlUKzSVwkqNVIjQEEpEqcD+6c1eyCzMIAIrqMSiMINQVxGPiYlF0R8lWhnSCwlRrajINypT\nprnc7NXfHz3tyScrt5+unrw+/92cc93num/H5dnx9ty/MEMxlIb9H8i0Wi1r16791WmIYczDw4OV\nK1f+6jTEEBs2M1tHdx4TYrDJZ3B4GzbFdiA7jwkxlOQzOLz9kXsjHDx4kAcPHmCxWNDpdKSkpBAb\nG9vvzmNCDAX5DIp/+yOLrRBC/G6GzTKCEEL8SlJshRDCBaTYCiGEC0ixFUIIF5BiK4QQLiDFVggh\nXECKrRB/27NnDzU1NT/tt3z5clpbW7/bnpmZyf379wcxs39cunQJo9E4oL55eXnU19cPSR7CcfKc\nrRD/h4KCAry9vUlNTR3ysT58+MD69evZvXs3ra2t7Nmzx9729u1bNBqN/fjAgQN0dnZy7Ngx9u7d\nO+S5iZ8b9hvRCPFfcevWLXx9fdHr9ej1ek6ePAnAq1evWLduHUajEaVSae8/evRobDYbT58+JTg4\n+FelLf4mxVZ8V0ZGBgsWLODq1au0tbUxY8YMMjIyUKvV1NTUUFVVRW5urr1/SkoKhw4dwsfHh4KC\nAjQaDa9evcJkMhEYGMjmzZuprKzkypUr6HQ6Nm7cSFBQUL9jl5SUcPPmTaxWKz4+PqSnpzN16lTg\n8ybclZWVVFdX8/r1a8aNG8eWLVsYPXo0d+/epbi4mI6ODmJiYmhqaiImJoa4uDjKysp4+fIlGzZs\nAP4pUn/99RdKpZKdO3cSHR1NXFwcL1++pLCwkIaGBlQqFdOnT2fTpk19rvPevXvU1tYCcOHCBaZN\nm0Z2djYZGRmsWrWKkJAQ3r9/z6lTp6irqwNg9uzZLF26lBEjRnD//n0OHz5MQkICZ8+exc3NjbS0\nNObPn9/vPamvr8dgMDj0MzQYDNy5c0eK7W9A1mzFD9XV1ZGTk0NBQQFNTU0DWtP8OjY1NZWioiJU\nKhXbt28nKCiIoqIiIiMjOXHixHdjg4OD2bdvH8XFxURFRZGfn8+7d+8AOH/+PNevX2fbtm0cP36c\nNWvWoNFo6OrqIi8vzz7m2LFjefTokVPXXVpaSmhoKCUlJRQWFrJw4cJv+sTHxxMVFUVSUhInT54k\nOzv7mz4VFRU8fvyYffv2sX//fp48ecKZM2fs7Z2dnVitVo4ePcrq1aspKirizZs3/ebU3NyMr6+v\nQ9fh5+dHY2OjQzFiaEixFT+0cOFC9Ho9o0aNIiIigoaGhgHHzpw5k4kTJ6JWq5k1axZqtZp58+bh\n5ubGnDlzePbs2XdjY2Ji8PDwQKlUsmjRIj58+MDz588BqKqqIjU1FV9fXxQKBYGBgXh4eFBfX4+/\nvz+RkZGoVCoSEhLw8vJy6rpVKhVtbW10dHSgVquZMmWKU+epra1lyZIl6HQ6PD09SU5O5tq1a/Z2\npVJJcnIyKpWK8PBwtFqt/Tr/rbu7m5EjRzo0vlarpbu726ncxeCSZQTxQ18XK7VajdlsdjpWp9P1\nOe7p6flu7Llz56iursZsNqNQKLDZbFgsFuDzPrBjx479Jqajo6PPmxAUCkWfY0csW7aM0tJScnJy\ncHd3JzExkdjYWIfPYzabGTNmjP14zJgxfe7hl18oX2g0mu/eF3d3d2w2m0Pj9/T04O7u7mDWYihI\nsRVO0Wg09q/18Pnr8GAxmUycO3eOHTt24Ofnh5ubGytWrLC/I87b25vW1lYmTJjQJ87Ly6vP5ty9\nvb19jrVa7YBz9vLyYvXq1QA8fPiQ3NxcDAYDPj4+ffopFIofXoter6etrQ1/f38A2tvbnd4wPCAg\ngBcvXjgU09LSQkBAgFPjicElywjCKQEBATQ3N9PQ0MC7d+8oKysbtHPbbDaUSiWenp58+vSJ8vJy\nrFarvT0uLo7Tp0/z4sULent7aWxsxGKxEB4eTnNzMzdu3ODjx49cvHixT0ENDAzEZDLR3t6O1Wq1\nv0q8P3V1dfZC/WVm2F9h1el0P3zmdu7cuVRUVNDV1UVXVxfl5eVER0c7fE8AwsLCePDggUMxJpOJ\nsLAwp8YTg0tmtsIpvr6+JCcnk5ubi1qtJi0tjcuXLw/KuWfMmEFoaCgbN25Eo9GQkJBgf908QGJi\nIu/fv2fXrl1YLBbGjx9PVlYW3t7eZGZmUlJSwpEjR4iJiWHy5Mn2uJCQEGbPnk1WVhYeHh4kJSVx\n+/btfnN4+vQpRqMRq9WKl5cXK1as6HfpIjY2lvz8fNLT0zEYDGzdurVP++LFi7FarWRlZQEQGRnp\n9IbhERERGI1GzGbzgGbHT548QavVMmnSJKfGE4NL/qlB/NG+fpzrT3D58mVaWlpIT0//ad+8vDxi\nY2MJDw8f+sTET8nMVoj/kPj4+AH3/TKbFr8HWbMVQggXkGUEIYRwAZnZCiGEC0ixFUIIF5BiK4QQ\nLiDFVgghXECKrRBCuIAUWyGEcIH/AWrZ8KidFDQkAAAAAElFTkSuQmCC\n",
       "text": [
        "<Figure size 676x442 with 1 Axes>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}